Dark Web OSINT with Python Part Two: … [Prizes For Unmasking Government Sites?]

Welcome back good Python soldiers. In Part One of this series we created a wrapper around OnionScan, a fantastic tool created by Sarah Jamie Lewis (@sarajamielewis). If you haven’t read Part One then go do so now. Now that you have a bunch of data (or you downloaded it from here) we want to do some analysis and further intelligence gathering with it. Here are a few objectives we are going to cover in the rest of the series.

Attempt to discover clearnet servers that share SSH fingerprints with hidden services, using Shodan. As part of this we will also analyze whether the same SSH key is shared amongst hidden services.

Map out connections between hidden services, clearnet sites and any IP address leaks.

Discover clusters of sites that are similar based on their index pages, this can help find knockoffs or clones of “legitimate” sites. We’ll use a machine learning library called scikit-learn to achieve this.

The scripts that were created for this series are quick little one-offs, so there is some shared code between each script. Feel free to tighten this up into a function or a module you can import. The goal is to give you little chunks of code that will teach you some basics on how to begin analyzing some of the data and more importantly to give you some ideas on how you can use it for your own purposes.

In this post we are going to look at how to connect hidden services by their SSH public key fingerprints, as well as how to expand our intelligence gathering using Shodan. Let’s get started!
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Expand your Dark Web OSINT intell skills!

Being mindful that if you can discover your Dark Web site, so can others.

This entry was posted
on Wednesday, August 10th, 2016 at 4:31 pm and is filed under Dark Web, Open Source Intelligence, Python, Tor.
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